`Being Canadian' and `Being Indian': Subject Positions and Discourses Used in South Asian-Canadian Women's Talk about Ethnic Identity
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Bibliographic record
Abstract
Ethnic identity descriptions can be viewed as `subject positions' (Davies and Harré, 1990) that are dynamically adopted and discarded for pragmatic purposes through the medium of socialinteraction.Inthe present paper, we use positioning theory to explore the multiple ways our participants—South Asian-Canadian women—positioned themselves and others in conversations about their ethnic identity. A discourse analysis of participants' talk revealed a tendency to privilege a `hybrid' Canadian/South Asian identity over a unicultural one. Moreover, in the rare instances when participants positioned themselves with a unicultural identity, subtle social pressure from conversational partners seemed to induce them to reposition themselves (or others) with a hybrid identity. We conclude by giving possible reasons for such a preference and by discussing the ways in which the current study corroborates and expands on the extant literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it